Short and simple arguments for why climate can be predicted

Sometimes, I encounter arguments suggesting that since we cannot predict the weather beyond a couple of weeks, then it must be impossible to predict the climate in 100 years. Such statements tend to present themselves as a kind of revelation, often in social settings and parties after I have revealed for some of the guests that I’m a climatologist (if I say I work for the Meteorological Institute, I almost always get the question “so, what’s the weather going to be like tomorrow?”). Such occasions also tend to be times when I’m not too inclined to indulge in deep scientific or technical explanations. Or when talking to a journalist who wants an easy answer. In those cases I try to provide a short and simple, but convincing, explanation that is easy for most people to understand why climate can be predicted despite the chaotic nature of the weather (a more theoretical discussion is provided in the earlier post Chaos and Climate). One approach is to try to relate the topic to something with which they are familiar, such as to point to empirical observations which most accept (I suppose with hindsight it could be similar to the researchers in the early 20th century trying to convince that nuclear reactions were possible – just look at the Sun, and there is the proof! Or before that, the debate about whether atoms were real or not – just look at the blue sky, and you look at the proof…). I like to emphasised the words ‘weather‘ and ‘climate‘ above, because they mean different things.

It is true that we cannot predict the weather indefinetely (or even beyond a couple of weeks), because of the chaotic nature and infinitesimally small uncertainties in the state as we know to day, will affect how the weather evolves in a few weeks (the ‘chaos effect’). But, still I say that I know with certainty that there is a very high probability that the temperature in 6 months will be lower than now – when winter has arrived (it’s summer on the northern hemisphere at the present). In fact, the seasonal variation in temperature and rainfall (wet and dry seasons in the tropics) tends to be highly predictable: the winters at high latitudes are cold and summers mild (if anyone doubts, read on here); the southeast Asian Monsoon usually starts over India in the first days of June. I don’t usually bring with me maps and figures to social events, but it would be nice to show a picture such as the one in Fig. 1 to illustrate. If the person is not convinced, I may continue with other arguments for why the climate is predictable: take the latitude for instance – the poles are cold and tropics warm. Furthermore, maritime climates at higher latitudes with wet and mild (small day-to-day or season-to-season temperature variations) are distinct to continental climates far away from the sea (dry with great temperature variations). It is well-established that high-altitude places tend to have lower temperatures and greater temperature variations. Most hikers and mountaineers have experienced that. These are local climatic properties that we can predict if we know the geography, even if we cannot predict the weather on an exact day far in the future. To convince further, I may add that empirical evidence suggesting that (local) climate is not unpredictable, but rather systematically influenced by external factors (boundary conditions) is that Northern Europe enjoys a mild climate: Oslo is roughly on the same latitude as the southern tip of Greenland. There is a reason for that – Oslo has a considerably warmer climate because of the effects of oceanic heat transport/capacity and prevailing winds. I also remind that people really have known for centuries that there are systematic factors influencing the local climate, it’s just that this fact sometimes gets forgotten by those who claim that we cannot predict climate. Isn’t it silly? I may ask if there is any reason to think that the predictability stops at the seasonal and geographical variations.

I may continue with in a hand-wavy manner: In a similar fashion as seasonal and geographical effects, changes in Earth’s orbit around the Sun alters the planetary climate by modifying the amount of energy received from our star (but because of terrestrial response, the atmospheric composition is modified as well, enhancing the effect even further), and changes in the atmospheric composition affects the climate because grenhouse gases absorb heat that otherwise would escape into space – greenhouse gases are transparent to sunlight, but opaque to infrared light due to their molecular properties and their ability to absorb energy (if I say it’s quantum physics, people tend to understand it’s getting a bit technical). I stress that the greenhouse effect is also beyond doubt – without it, the energy balance between total energy Earth intercepts from the Sun and the energy lost through black body radiation implies that Earth’s surface on average would be about 30K cooler than we know it. Volcanoes also affect our climate, and we have theories explaining why. Furthermore, looking to other planets, the observation that Venus has higher surface temperature than Mercury, despite being further away from the Sun, can only be explained as a result of different absorbing properties of their respective atmospheres (a strong greenhouse effect at Venus).

So, my question is, do you think people get the message that I try to convey this way? Is it too simple or too complicated? Somebody who knows of every-day examples demonstrating the central principles? Any suggestions on how to explain for laypersons not connected to the Internet?

219 Responses to “Short and simple arguments for why climate can be predicted”

All your explanations make sense, and to a listener just interested in learning, they should settle the matter. But sometimes people get ideas which just seem too good to give up, no matter how clearly you explain why they are wrong. This can lead to more and more contrived arguments to try to save the argument. I have done things like this in my mathematical work and I’m sure that all scientists do it on occasion. But an honest scientist eventually has to give up when it becomes clear the idea can’t be saved. A non-scientist however, not really understanding the subject, is not bound by such limitations and treats it more as a debate than a search for the truth. And of course sometimes scientists do the same. Thus are born denialists.

Another good way of explaining it may be to point out that climate scientists are not trying to predict precisely which future days and which locations will be warm, they are only predictinng that the average temperature of all days combined will be warmer then currently. Maybe the cocktail crowd will intuitively understand that it is easier to say with confindence that the next decade will be warmer on average than in the past, than it is to say with confidence that next June 17th’s high in Los Angeles will be 97 degrees F.

Weather is like the hour-by-hour fluctuations in the Dow Jones index.Predicting that is hard. Predicting climate is more like predicting the longer term trends, except,even even easier since the forcings tend to be better understood ! (i.e. intrest rates -vs- GHG forcing).

Here’s another possible perspective. The increase in hurricanes and hurricane strength is at least partly due to warmer SSTs. The other influences are global and regional weather patterns which change the shear, dry layers, and other factors. It’s important not to oversimplify the mechanics, and thus important not to make oversimplistic predictions like more and stronger storms this year. Otherwise an amateur skeptic might say “aha!” when there’s fewer storms like this year so far.

The better approach is to emphasize the long term, emphasize that storm strength is only one result of climate change and perhaps a minor one at that. Better to do that than to get front page Time magazine stories about megastorms or something like that.

IMO, we can predict the weather beyond couple of two weeks. It is true that the prediction error increases and the effect due to initial conditions (state of system today) should get less weight than the local weather history, but it doesn’t mean that we cannot predict.

I agree with L. Evens. D. Donovan makes a good point except no one can predict, with better than 50% chance of success, the long-term dow, either. One of the difficulties is that the climate models do in fact have considerable holes and produce a significant amount of uncertainty. If one admits to that the antagonist says, “SEE! I told you so!” But if you don’t you become one of those denialists. None-the-less the basic premise of the treatise is correct (short term specifics and long term trends are completely different from an analytical and scientific view) and I think explained very well. Admitting to the modeling uncertainty may advance your credibility; beyond that I don’t think any “improvement” in the assertion will change the outcome at the party.

Beginning in the 1970s/1980s, the National Weather Service Climate Prediction Center began issuing one and three month outlooks for precipitation and temperatures, in relation to the latest 30 year averages (normal).http://www.cpc.noaa.gov/

Some people have said that there is only a 50/50 chance for outlooks calling for above or below normal to be accurate on temperaratures. Not so for northern Minnesota in the future with rapid climate change happening. The increasing Dec-Feb temperature trends are so obvious in northern Minnesota that I can predict right now with a high degree of confidence that the winter of Dec 2006 to Feb 2007 in northern Minnesota will once again be above 1971-2000 temperature averages, and will continue to be above the 1971-2000 temperature averages for hundreds of thousands of years to come.

Rasmus, here’s my rough outline. When predicting climate for the whole world you don’t need to know whether it will snow in Oslo on December 20th 2006. The climate record from Oslo tells us that it could, and more importantly, the climate model predictions show that it could. The climate models also incorporate the effects of forcings like man-made CO2 and are able to predict regional changes like the prevailing winds or ocean currents and their effects on local climate in Oslo.

I would conclude that a simple climate model without weather can do a fairly adequate job of knowing the average weather in Oslo for the time period around next Dec 20th. Is that sufficient for accurate climate predictions? No. The reason is that the physics of the model depends greatly upon accurate depiction of weather. This is because the primary warming mechanism, far beyond all others, is water vapor feedback and water vapor is controlled by weather. Some of the other lesser effects like snow cover are also controlled by weather.

So models like CCSM and ESMF incorporate weather by simulating its effects in coarse spatial and temporal resolution (e.g. 40km and 30 minutes). The purpose is to establish realistic weather patterns from the climate parameters and use the resulting weather measurements to give realistic feedback to the climate model. Higher resolution might be required for tropical weather than for Oslo. The resolution is required to adequately depict climate-effecting weather, that has been studied and explained in the CCSM papers.

Here’s a simple example. An average weather pattern for Oslo might be a day of snow and 4 days of partly cloudy repeated. But it could snow for five days in a row. This weather phenomenon will clearly affect subsequent local climate (temperature, remaining snow cover, diurnal clouds, etc) and all weather phenonema across the globe will affect global climate.

Good explanations but in these settings I always think that short and simple is the key. So I just agree that you can’t predict the weather all that well but say that you can predict the season, e.g., chances are it won’t snow in August, and predicting the climate is more like predicting the season than the weather. Then I leave it at that unless they want to continue.

The question is whether climate in principle is predictable. All the examples presented in the post used measured data and actual physical realizations; no mathematical models or computer programs are involved. These are not examples of predictions of either weather or climate in the sense of using models and codes for long-term time scales in the future. In fact, no calculated results (predictions) have been shown to compare with the measured data. In this sense the title of the post is very likely misleading.

Here are two issues associated with AOLGCM that I find to be totally unique to the applications of these codes.

Firstly, it is my understanding that the calculated results from most large complex AOLGCM codes cannot be demonstrated to be independent of the discrete representations of the continuous equations. That is, the results are functions of the size of the discrete representations (or truncated series) of the spatial and temporal scales used in the calculations. Independence of the discrete representations so that the solutions of the discrete equations converge to solutions of the continuous equations is the most fundamental concept taught in every numerical methods textbook; every textbook without exception. In the absence of this property the discrete equations have not been solved; the numbers printed do not represent solutions of the discrete equations. The lack of grid independence indicates that numerical errors are in fact present in the numbers. I think this situation is without precedence in all of science and engineering. If this is the correct situation, the calculated results are not solutions to the continuous equations and at the very best represent some kind of approximate “solution”. However, “solution to the continuous equations” is not a phrase that can be applied to these calculations. No other science or engineering applications of numerical solution methods tolerate this situation; it is always unacceptable. An example of an exception to my assessment will be greatly appreciated.

The closely related issues of consistency and stability are also very important.

Secondly, it is my impression that use of ensemble averages of several computer calculations that are based on deterministic models and equations is unique to the climate-change community in all of science and engineering. I can be easily corrected on this point if anyone can provide a reference that shows that the procedure is used in any other applications. (The use of monte carlo methods to solve the model equations is not the same thing). The use of ensemble averaging and the resulting graphs of the results makes it very difficult to gain an understanding of the calculated results; rough long-term trends are about all that can be discerned from the plots. The calculated daily, seasonal, and yearly variations, some of which are used in the post, are seldom compared with measured data. Neither are calculated results from various spatial locations, also used in the post, compared with measured data.

No other modeling applications in all of science and engineering will have these two characteristics. Bridges, elevators, flight-control systems, power generation systems, (add your favorite here); no simple or complex systems of any kind. Again, examples of exceptions to my assessment will be greatly appreciated.

Focus on The Global Average Temperature calculated by the codes gives a false indication of the robustness of the modeling and calculations because this is a solution meta-functional result that maps everything calculated by the code into a single number. A process that easily hides an enormous number of potential problems.

[Response:Actually, the approach for climate prediction is very similar to that used for weather forecasting, where indeed the predictions are compared with the outcome. The criticism of the discretisation applies to numerical weather models (NWM) as much as to general circulation models/global climate models (GCMs). Furhermore, ensemble forecasting is also used for weather forecasts. Despite the involvement of the range of different scales, the models produce useful results (on which aviation depends). -rasmus]

Making climate projections is very limited to input of data, some of which from sparse places on Earth, lacking resolution, GCM”s give ultimately bad weather prognosis. Same applies with Climate models, without very high resolution inputs equally applied at every point on Earth, it’s nearly impossible to be correct. Thus probability methods are used similar to quantum mechanics, with expected results. Dedication must be given to increase data resolution , as this is done, climate and GCM forecasts will be greatly enhanced. But there are also experimental methods, which are ignored, I use one of them, to make projections based on absolute or total atmospheric temperatures, independent of computer models and getting accurate results. These methods (must be many more I don’t know about), will greatly increase accurracy as well, and perhaps there is a lack of experimentation in the field, a bit of conservativism, more hurting, in the long run
the reputation of models than anything else. Heard that some long range seasonal forecasts are done by taking the average of 11 or more Climate projection runs, as Einstein might of said: there is no such thing as luck.

Anyway, for 20 years in February and March of each year, I put together the NWS Spring Snowmelt Flood Outlooks for the Upper Midwest based on ‘normal’ temperature sequences for runoff in late March and April.

By the late 1990s it was clear that the timing for snowmelt runoff had changed. The melt was occurring earlier in the season, and the odds for having rain during the melt period was higher. I knew that climate was changing even before I knew that global warming was happening.

I have no experience with global climate prediction models, but it’s obvious to me that as climate changes more rapidly it begins to have significant effects on weather and hydrologic prediction, particularly if the models used for those predictions ignore the obvious climate change factors.

Re #12: Pat, thanks for fixing the link. I found your post on the twin cities site very helpful, as are your posts here. The snowmelt/runoff situation is essential to follow to understand and predict what is happening out west, where it appears that there have been fundamental changes.

You might want to concede that we cannot predict the climate 100 years from now, but point out that it is nonetheless important to have a practical understanding of the factors that influence climate. Then, give examples, starting with the “factors influencing the local climate.”

I make this suggestion after reading a related post, “Beyond the Mug’s Game,” on Prometheus (Pielke) about the value of economic models. It points to an interesting response (letter to the editor) to an interesting article on economic models in The Economist.

The gist of it is that complex economic models can be more useful at informing people than they are at predicting events. The letter and the article are readable and, to me, not as boring as their titles suggest.

Suppose that a ping-pong ball falls into a river. Due to the turbulent eddies in the river, predicting the ball’s EXACT position, e.g., within a few meters,is possible only in the short-term (in this case, over the next few seconds), but not in the long-term. However, if we knew the average rate of flow, we could predict reasonably accurately (within a few minutes) when the ball would pass under bridge 10 km downstream. Futhermore, if the streamflow were increased by a known amount (due, say, to increased discharge from an upstream dam), I doubt that anyone would argue with a prediction that a second ball would take less time to reach the bridge,even though it may not be possible to say exactly under which part of the bridge the ball would pass.

The question is not whether predicting the climate for the next 100 years is a reasonable undertaking. The question is, can such a prediction have the absolute certainty on which we can invest our treasure with the confidence that it will not be wasted. Put another way, has anyone ever successfully predicted a change in the climate of the earth over a similar 100 year period with our exact starting point (i.e. current concentration of greenhouse gases, state of human technology, current population of the earth, current land use, etc). You can show me a nuclear reaction over and over again and you can show me that an atom is real over and over again, no faith in your predictive capability is required. Can you offer proof that you have successfully predicted the climate for 100 years. Can you offer proof that just adjusting CO2 in the atmosphere will control the climate the way you predict

Which can you predict (to within say, 10% or 20%): the number of times heads comes up on a single coin toss, or on 100 tosses? The total number of runs scored on a particular baseball (or soccer or whatever) game, or the average number over a whole season? The height of the next person to walk around the corner, or the average height of the next 100 people?

Or this: you can’t predict where a particular leaf, or grain of sand, will end up after a wind storm, but anyone can easily predict that the entire planet Earth, leaves, sand, and all, will continue to spin on its axis once a day, and revolve around the sun once a year.

Folks may be amazed to realize how often they can predict big complicated, things much more accurately than small, simple things.

Predicting climate is like predicting what will happen when you boil a large pot of water. The modeler would need to know the heat input, the heat transport characteristic of the pot, the starting temperature of the water, the ambient temperature, atmospheric pressure, the contaminants in the water, etc. But anyone with a good knowledge of physics could predict approximately how long it will take to boil, what range of temps and what kinds of convections might arise.

Predicting weather is like trying to predict temperature and current flow in the pot to scales of a 1 cm.

I think its clear you can predict that the water will boil (climate) and even approximately when it will start given a few good measurements. Predicting temperatures and current flow to small scales obviously is only possible for a few seconds at a time given only with really good measurements of things exactly as they are.

[Response: A slightly different version of this analogy has been around for a long time (I don’t know the original source) and has been used by myself in talks and a couple of popular articles: predicting climate is like predicting that the boiling water in the pot has a temperature of 100 ºC at sea level, but only 90 ºC at a particular altitude in the mountains. Predicting weather is like trying to predict where the next bubble will rise in the boiling pot. This illustrates that the former is a boundary value problem – the mean temperature of the boiling water depends in a predictable way on boundary conditions, i.e. air pressure, just like the global mean temperature depends on the CO2 concentration. The latter, in contrast, is an initial value problem in a highly turbulent system and therefore a much harder problem. -stefan]

You haven’t followed your reasoning to its obvious logical conclusion. Suppose in fact that models can’t tell us with certainty the consequences of our ongoing experiment in increasing concentration of greenhouse gases. Since there is certainly no other way to answer that question, it would apprear foolhardy in the extreme to continue that experiment. I hope you are insisting to your governmental representatives that they take action immediately to limit greenhouse gas emissions.

My reply is to offer a bet: I will predict the average temperature of the earth for next year and they will predict the temperature of the city we are in for that day next year. While I have had a number of very creative reasons for they will not agree to the bet I have yet to have anyone take me up on it.

Re: #17 -“Suppose in fact that models can’t tell us with certainty the consequences of our ongoing experiment in increasing concentration of greenhouse gases.”

The so-called ‘experiment’ is an agreeable metaphor, especially for the scientifically-minded. But what is sometimes called an ‘experiment’ is actually a social adaptation, and since the adaptation is carried out by clever though not always rational bipeds instead of ants or termites, the components of the adaptation are immensely complex. Complex as these adaptations may be, anyone who has watched the long slow procession of late afternoon traffic queing into I 395 in Virginia out of DC will be able to conclude, easily, that social adaptations are not always rationally arrived out, even in homo sapiens: Wise guy.

The problem with my scientific friends is that they assume that given a spectrum of choices, human beings will naturally choose the most ‘rational’, meaning, the most obviously adaptive for the greatest number. They won’t. They can’t. That’s why societies, like species, always have finite lifetimes.

Adaptation in a developed early 21st century society means maximization of profit. The reproduction of money takes the place of the reproduction of descendants in the lower orders.

Make it more profitable to supply energy without changing the climate, and you will have solved the problem of climate change. Either that, or wait for the first really horrific crisis and see what happens. Don’t count on a rational response, though.

Gavin, I have noticed in my own work that when having difficulty explaining a complicated scientific idea in easy to understand terms for a non-scientist (like my wife), often I do not have a good command of the concept myself. In these instances, I may need to do some internal clarification. It is my suspicion that this is where you are on this issue (be honest with yourself).

Regardless how the IPCC defines the term “climate” or “climate system”, the issue is the actual manifestation of the system we are trying to model, and whether or not the real-world observations support our conclusions about its theoretical state. It is clear to many of us out here that you are laboring painfully to propogate the troubled hypothesis that the system you are trying to model is well-behaved (non-chaotic), therefore inherently predictable over multi-decadal time. In this respect, #1 may have a good analysis of your continued stained attempts to simplify this matter.

The new OHCA data presented by Lyman etal might at least give you the opportunity for retrospection. Many look forward to your coming analysis of this new paper, explaining the failure of the models to predict the 21% (likely) loss of heat (in two years) that was accumulated in the system from 1955-2003. This occured while you declared loudly in 2005 (from the model predictions of this well-behaved system) that the system was in a positive radiative imbalance. We will now see if these new results hold up under the coming tsunami of scrutiny. Return of the Jedi!!

Hey, Gavin. I agree, coming up with an “elevator pitch” reason for why folks should put some faith in the models is a good idea. I find that people are most rational about is money – everything else is up for grabs. For instance, laypeople have a hard time remembering that energy is conserved in all physical interactions – the first law of thermodynamics. Explain it to them in terms of money, and they get it instantly: “Oh, you could make free money by selling the energy you got for free – okay, that’s stupid then.” (Ironic, since money is decidedly *not* conserved at the level of governments.) This leads to approve of the “bet” way, #s 18 and 19 by John Cross. Another tactic is point out the silliness of their argument: “oh, you can’t predict anything about the weather out more than a few weeks, eh? I predict that in mid-January it’ll be colder than it is today. Think I’ll be wrong?” That works for me in Ottawa, your mileage may vary.

I think any discussion of climate (to the lay) must include scale– both spatial scale and temporal scale. The current hot item is “global climate” at annual and decadal scales. But it is important to note that every point on the globe has a climate, and the points can be aggregated to regions, regions to continents, continents to hemispheres etc. And it is essential to note that you must “generalize out” (in the vernacular of cartography) some degree of detail every time you scale up. This is intuitively obvious to most but it never hurts to explain.

The temporal scale is another tricky item. People are currently hooked on annual temperatures, especially the annual mean daily temperature. This is the crudest information we can get over the course of a year. Providing slightly more detail is the mean high temperature in tandem with the mean low temperature. We can move to finer temporal intervals (seasons, to months, to days etc), and each time we do, we increase the detail of our understanding about the place(s) in question.

Also, it is helpful to recognize that we use climatic information to place weather into context. So, even though we want to avoid confusing weather with climate, we need to keep in mind that we require climatic information in order to identify daily weather anomalies. I only know that a certain day was “warmer” and “wetter” than average Minneapolis because I have climatological baseline values for comparison.

And of course, everyone who works with climate has his/her own, idiosyncratic definition of climate that falls somewhere along this spatio-temporal continuum. For example, I work on “events” of local or regional scope (on the order of tens of km^2 to tens of thousands of km^2), lasting days at most, which are then placed in the context of the preceding 30-120 years. One member of my Ph.D. committee does dendroclimatological work, and is therefore often interested in large chunks of continents, with whatever is resolved at the annual level placed in the context of decadal, multidecadal, and centennial patterns of the past several hundred years.

In terms of predicting the climate, the very general (e.g., the mean annual global temperature for a given year) should always be somewhat easier than the more specific (e.g., mean temperature of New England for a given March), so we should have less confidence in predictions that have more detail.

Your explanation is fine, but it still needs more of a common touch. Depending on whom you are talking to, you might be able to make an argument from sports. The point you are trying to make is that predicting averages is easier than predicting details. Thus: you can’t predict exactly who will get which hits in the all-star game, but you are pretty confident that the AL will win.

Robin #19, the types of convection in the pot and the temperature distribution don’t really matter because there is no greenhouse effect in the pot so the physics is much simpler. The earth’s convection does matter because it distributes the water vapor which is the primary GH gas. Perhaps your analogy could be extended to a pot with a dynamically changing lid, but I’m not sure how to express that concretely.

In the absence of comparisons of calculated results with measured data you cannot be sure that you can ‘predict’ anything. Additionally, the calculated and measured data must be in agreement within some level of differences. While averages are somewhat easier to get more nearly correct, these values can also be calculated incorrectly. Show the caculated results with the measured data and then there is a possibility that you have shown that you can ‘predict’ physical phenomena and processes.

Here’s an analogy to consider for your next party or elevator trip. Ask the following:

“Do you invest in the stock market?”

For most, the answer is yes. Then say:

“OK…a financial advisor can’t tell you if the market it is going to up or down tomorrow, but you trust him when he tells you the influences on markets are such that they will probably go up in the long run. And there’s historical precedent. That’s why stocks are such a popular investment.”

“It’s the same thing with climate prediction. Just because we don’t always get weather right means NOTHING about our ability to predict climate. Assuming we understand the influences on climate (past, present, future) as well as we understand market forces (we probably understand climate *better* for predictive purposes), we can provide general predictions about the climate’s future course.”

I like the analogy in #16. Here’s one I use: Why is global climate changing? Because there’s more heat in the earth system. Imagine you have a very wide and shallow pan of water on a burner; the pan sticks way out on all sides of the burner. The water in the middle of the pan will heat up first, then move toward the cold water at the edges–that’s how fluids such as air or water work. That’s how you get weather– “differential heating.” But, then, suppose you turn up the burner a notch. Now, there’s more heat in the “system.” Water will still heat up first in the middle of the pan and then move to the edges, but it will be hotter and move faster. That’s like global warming due to more radiation being absorbed by the system (“radiative forcing”).

‘Re: #17 -“Suppose in fact that models can’t tell us with certainty the consequences of our ongoing experiment in increasing concentration of greenhouse gases.”

The so-called ‘experiment’ is an agreeable metaphor, especially for the scientifically-minded…’

The numbering keeps changing which makes it difficult to keep track of the discussion. I hope my quote within above indicates the history.

My initial point was that arguing against making changes on the basis of uncertain models is fallacious. The error is in thinking that the status quo is stable. Even if that were the case from the point of view of social and economic forces, it is highly unlikely to be stable from the point of view of the radiative characteristics of the atmosphere. Whatever faults our models have, no one has been able to show conclusively that the large scale changes in greenhouse gas concentrations that are in the works are so unlikely that we may not concern ourselves about them.

I do agree that ‘we’, whoever that may be, are not conducting a purposeful experiment to see what the effect of greatly increased greenhouse gas concentrations will have. But the atmosphere doesn’t respond to our intentions; it responds to what we do. The question is whether or not ‘we’ can change what we are doing. Pavel, on the one hand seems to adopt a social/economic determinism which suggests it is all beyond anyone’s control, but then contradicts that by suggesting that making it profitable to do otherwise would resolve the problem.

That of course is also based on a metaphor. One can argue endlessly about whether or not ‘free will’, the ability to make choices, exists either on an individual scale or on the scale of societies. But whether or not social choices are truly free, it is a necessary metaphor to consider them so. Those of us trying to convince our fellow human beings that action is necessary may ourselves be acting as a consequence of impersonal social laws, but that doesn’t mean we should just stop trying.

The real issue, as a practical matter, is whether or not the human species can/will change its behavior in these matters. I think that is not settled yet. There are examples of where the short term local motivations were successfully countered by long term concerns. When I was young, you were an oddball if you didn’t smoke. Nicotine is highly addictive, and the sale of cigarettes became very profitable for tobacco companies. But in spite of that, medical evidence about how our physical bodies actually responded to smoking, eventually led to change. Today, in much of the US, you are an oddball if you do smoke, and at least one cigarette company advertises the dangers of smoking. The case of CFCs and ozone is also illuminating. Once Dupont’s chemists convinced their management there was really a problem, ‘we’ switched to other refrigerants, despite significant cost and some inconvenience. The same arguments were made against doing anything by Fred Singer and other skeptics. They tried to pick holes in the science and claimed that phasing out CFCs would be highly disruptive. In fact, it was done relatively easily. It is true that cutting back on greenhouse gas emissions is a much harder nut to crack, but it is certainly not clear at this point that ‘we’ can’t manage to do it. And looking only at ‘profit motive’ as the way it will be done assumes unproven theories about how humans behave en masse. Today, free market apitalism is certainly an extremely important principle in understanding how human societies operate. But, that all human behovior is reducible to such analysis is also a metaphor and a not very good one at that. Consider for example why so many Americans drive low mileage SUVs. It certainly can’t be because they make sense economically. The reasons are complex, but it is clear that fashion plays a signficant role. The practical functions they provide to families were once provided by station wagons, which did much better in terms of gas mileage. But one thing is pretty clear. Congress provided a loophole to mileage standards car manufacturers could drive their ‘trucks’ through. Without that, we would be using relatively gas efficient station wagons. The same thing didn’t happen in European countries, and I don’t think it was inveitable that it had to happen here.

It was argued above that climate models were exceptional among numerical models used in science and engineering in that neither can they be shown to converge nor, if so, can they be shown to converge to solutions of the equations they are meant to model. I think what the previous poster said is in fact wrong, and it is quite common in numerical modelling for this sort of thing to occur.

I would like to see a response from a bona fide climate modeller.

I am hardly an expert in numerical modelling. But I have at times in the past tried to undersand something about the subject. It seemed to me that error analyses for numerical procedures, in order to make useful predictions, had to make assumptions which themselves could not be proved. In practice, the results of using the procedure were monitored to see if they were plausible in known situations and also to see if the original unproven hypotheses seemed to be holding. From the point of view of rigorous mathematics, the latter approach is clearly circular, but in practice it does seem to work. In the case of climate models, I think the situation may be even more complicated because one doesn’t try simply to model a set of equations all of which fit into a specific mathematical/physical formalism. One tries to model the actual physical system and that incoroprates other features dependent on chemistry and biology. In addition, different, mathematically distinct physical theories are modelled. In principle, you could use quantum mechanics to describe the wave function of the atmopshere and try to model that. In practice, you use classical fluid mechanics and thermodynamics for some things and quantum mechanics for others. These are entirely different mathematical theories involving different equations about different physical entities. What holds them together is the belief that these are all different ways to describe physical reality. Hence, the ultimate measure of the success of a model is not necessarily its mathematical behviour but the extent to which it is consistent with observations of that reality.

As a somewhat related aside, mathematicians have always been bothered by the cavalier way physicists argue, using series that don’t converge or integrals that are infinite. This doesn’t bother the physicists at all if they can by hook or crook pull out numbers which both match observations and fit into an overall conceptual understanding of what is going on.

Rasmus, you ask “So, my question is, do you think people get the message that I try to convey this way?”

I think that the answer is “No”, they don’t get the message. And I think that is why you are asking for help. You can see that they are not convinced.

I had thought that one reason they are not getting the message, is because you are answering the wrong question. So the message you convey is irrelevent to their question. The question they are really asking is “How can we trust your predictions, when so often even the weather forecast is wrong?” Unfortunately that question is even more difficult to answer.

It is all very well saying that saying with more CO2 then the world will get warmer just as it does in summer. But you have been stabbed in the back by the oceanographers who say Britain and Norway will cool as the world warms, because the ocean currents will switch just as they did in the Younger Dryas.

Moreover, those of us who were around during the seventies know that then there was a scare about an imminent ice age. See http://www.cpc.ncep.noaa.gov/products/outreach/proceedings/cdw29_proceedings/Reeves.pdf
Although Kuckla and Matthews got it wrong, there is a widespread belief amongst younger scientists today that scientists are infallible. I would trust their views on scientific matters more than those of the Pope, but science is only true by definition. The utterances of scientists are are no more true than those of the Pope until proved so, and perhaps not even after that eg Einstien’s revision of Newton’s work. Young, and some not so young, scientists seem to think that if a paper is published in a peer reviewed journal, then it becomes science and hence it is true. That is not the case, and now new evidence is showing that the breakout of Lake Agassiz did not coincide with the start of the Younger Dryas, and hence cannot be the cause, despite inumerable scientific papers claiming or assuming that it was.

So how can we trust the scientists to be correct about global warming? Well, although they may not know the cause of the Younger Dryas they do know that it happened. What they do know is that the climate changes rapidly. That discovery was what scared Kukla and Matthews. In a cooling world they feared a rapid cooling. Now we are in a warming world the danger is of a rapid warming. That would disrupt agriculture almost as much as an ice age, and without food people cannot exist.

So the message should be, “We cannot predict the future climate. However, we do know that a rapid warming is likely and we do not know when it will happen. It could be soon, so we must take action now!”

I know that you will find it hard to admit that you are ignorant, and that your fellow scientists will find it even harder when they have to admit they were wrong. But the only way to convince people is to tell them the truth. Lying about the ice age scare only makes the public less trusting of scientists, when they know that it happened. Pretending that the models will give the correct results is equally false. We will only know that when the time comes, and they have proved that they give the right answer. (Since there is such a wide spread of results, surely one will be correct :-)

[Response:Thanx for your views. Rather than looking at the skill of the models (detailed predictions), I have looked at the principle of whether the climate is predictable (initially without involving the models). If you ask how skilful the models are, then one needs to define a metric defining the skill – or ask what the model is used for. I’re right that in our case one important task is to predict the future climate. You can then look at hindcasts (forecast made for the past, but performed in such a way as if it were in the future so that the observations against which you want to evaluate the model are not used in making the predictions. If you look at the global mean temperature, then you see a fairly good ‘re-construction’ of past trends, given the past emissions, volcanic activity and solar variability. I think it’s true that we cannot make a proper prediction for the future, as we don’t know what drivers such as economic activity (emissions) will be like in the future, not do we know when there will be volcanoes, there will be further landscape changes, or if the solar activity will change. But we can still make useful scenarios to get plausible idea about what to expect. One thing that has increased my confidence in the climate models is that features that are observed in the real world, such as El Nino (ENSO – some models do not give as good a representation as others though…), Hadley Cell, the North Atlantic Oscillation Kelvin waves, Rossby waves, and Tropical Instability Waves (TIWs) drop out of these models (if I dont remember incorrectly, I think the TIWs were first predicted in the models before they were seen in the real world). There are other aspects that are not so well predicted (e.g. the south Asian Monsoon, the Madden-Julian Oscillation, other major uncertainties involve clouds, and this has received a great deal of attention in IPCC, TAR.), but it is still impressive how the models represent these features – they arise purely from the physical equation, and are not prescribed’… even if some matematicians may think that physicists are cavalier about their application of equations. -rasmus]

Re Your response: Despite the involvement of the range of different scales, the models produce useful results (on which aviation depends). -rasmus

The weather models do not get the cloud base right, but because they can be tested each day, the meteorologists know that and can make the appropriate corrections. But no one knows what corrections need to be made for a climate model looking 50 years ahead, because we have never been there!

[Response:The corrections are for systematic biases, as the geography/topography representation in the models is fairly crude, and the model results represent a volume whereas observations tend to be more ‘point measurements’. I think it’s true that models do have biases in their representation of the vertical profiles in the boundary layer (I’m not really an exert on the topic of boundary layer representation in models…), and clouds tend to be crudly represented (as unresolved parameterisation schemes in climate models). Besides a representation of the cloud base would be limited by how many vertical layers the model has and how dense these are. Another side of the story is that the cloud base height depends on the temperature profile and the humidity (converctive systems), or the structure of weather fronts. Observations for cloud base on a routine basis presumably come from radio sondes, by the way? How big are the errors, you reckon? By the way, for the future we can used empirical downscaling to get an improved representation of local surface variables. These predications can be – and are – tested in hindcast experiements on independent data. -rasmus]

Pat, I should have been more careful in my post 23. The link you gave in 14 includes this quote:

â??18,000 scientists signed a petition saying the global warming model as presented in the popular media is just plain wrong,â?? said Pekarek.

That has to be referring to the 1998 petition drive he was involved in, as per my post 23. That effort was discredited years ago, not months ago.

Has he published anything in the peer-reviewed literature that supports his assertions? If not, I think the explanation to others is that “Patarek is simply tossing out untested personal opinions that have not been evaluated by the scientific community.”

#26. Thomas, thank you for highlighting the fact that my attempt to lighten the mood with a little humor failed. All humor aside, the Lyman etal results http://www.pmel.noaa.gov/~lyman/Pdf/heat_2006.pdf
in fact raise some profound questions about the predictive skill of the models, and thus relate directly to this subject.

In the absence of comparisons of model predictions with measured data you cannot prove that you can predict anything.

It is often stated that the GCMs solve the basic equations of mass, momentum and energy conservation. So long as the results cannot be shown to be independent of the discrete representations of the continuous equations there are errors present in the numbers. All textbooks concerned with numerical analysis/methods will state this fact. The numbers do not represent solutions to the basic equations. Additionally, the predictive power of the models does not mainly reside in the basic equations of mass, momentun and energy conservation. The algebraic parameterizations carry the heavy work of getting the physical phenomena/processes nearly correct.

RE:#39 and also the original question. i think one of the most basic problems with the public in understanding climate prediction is the question they have regarding will global warming cause the collapse of the Atlantic circulation and hence a new ice age? or will temperatures on the average get warmer and warmer compared to what we have experienced. That’s is about as unpredictable as it can be for them and I think feeds the feeling that there is nothing that can be done about it, it’s all “wild” speculation.

Re #44 The only way we are going to get the public, and through them the politicians, to take action is to scare them to death. It was fear that got the millenium bug fixed, it was fear of the ozone hole that ended CFCs. It is fear that has mmeant the end of nuclear power, and fear that prevented a nuclear war. It is fear of terrorism that allowed GWB to take the US to war in Iraq.

The dangers from global warming are far worse than those from nuclear power stations, or the ozone hole but the scientists are not explaining that! How many people have died as a result of the ozone hole, or nuclear accidents, ot from terrorist activity. The heat deaths’s in France during summer 2004 far outnumbered anything from even the worst terrorist attack. As Gavin wrote, scientists are treating global warming as ‘It’s serious (and interesting) but don’t panic’. Nothing will happen until they say ‘It IS serious and I AM panicking.’

They don’t have to make up ‘scary stories’. Just point to the hurricanes, droughts, floods, and fires around the world, and explain that they will only get worse, and lead to famine. There is no easy fix if you don’t have enough food. And they must point out that it is only 10,000 years since temperatures climbed by 20C in Greenland over period of less than 40 years. If only they would have the courage to tell the truth!

#28, In a way Global mean temperatures, at least in my case for the Northern Hemisphere, is a bench mark, of which everything else transpires, it would be in fact very good to have one predicted for each coming month, aside from the usual GCM probability inspired seasonal forecast map. I like to see, if the models can in fact predict the most simplest, but most important parameter, if they do they are on the right track, if they don’t, it would be quite revealing. I remember with great disgust in October 2005, last falls forecast for a cold bitter coming winter. Was not sure if the radio announcer was on the same planet.

I’ll agree with L. Evans (#1); one of the critical aspects determining whether or not you’ll succeed in getting your point across, is whether or not the listener is open-minded.

The analogy I often use is the height difference between men and women. I point out that there’s no way to predict with much confidence who will be taller, the next man to enter the room or the next woman. But we can predict with *extremely* high confindence that the average height of men at the party is greater than the average height of women at the party.

When I tell this to the open-minded, they get that “oh – I get it!” look on their faces. When I tell this to the closed-minded they invent a reason to invalidate my analogy (sometimes very creatively, sometimes ridiculously silly).

I can usually tell, even before I offer the analogy, whether or not the listener is receptive. But even for the non-receptive, I offer the analogy anyway. It may not influence their opinion, but there are always other interested listeners, and they often seek me out later to find out more.

Most certainly. If I gave the impression of a sort of fatalism permit me to retract. In the case of stratospheric ozone depletion there was an obvious global emergency in progress, and a ‘fix’ that was not so disruptive as to cause exceptional distress, or for that matter a decline in profits.

There is an analogy here to the problem of GW greenhouse gases, but not an identity in terms of social scale, tempo or predictability.

As a Catholic I don’t hold that economic determinism is the main moving force in human societies.

Nevertheless, if you want change you must find the appropriate operational handle.

A good start for a discussion about AGW is by using examples and metaphorism.
One example can be found in James Hansen *Defusing the Global Warming Timebomb published 2004 James Hansen Archive

HUMAN-MADE climate forcings, mainly greenhouse gases, heat the earth’s surface at a rate of about two watts per square meter – the equivalent of two tiny one-watt bulbs burning over every square meter of the planet.
The full effect of the warming is slowed by the ocean, because it can absorb so much heat. The ocean’s surface begins to warm, but before it can heat up much, the surface water is mixed down and replaced by colder water from below. Scientists now think it takes about a century for the ocean to approach its new temperature.

If we are conducting an “experiment in increasing concentrations of greenhouse gasses”, it is also pretty clear that it is not a controlled experiment, and that the 0.6 degrees C warming over the last century has occurred in the context of one of the highest levels of solar activity in the last 8000 years.

Yes, climate has been relatively stable and predictable for a couple of centuries, and even the changes over the last few millenia, while locally significant, have not changed the overall pattern. Absent, mode changes or tipping points, even the most sensitive models do not predict changes in climate patterns over the next century.

However, just because climate has order and modes, and thus is predictible, does not mean that we can predict it with current model technology.

Since the recent warming is apparently due to net globally and annually averaged heat fluxes of under 1 W/m^2, understanding and attribution of the relative causes of that warming given the number of inputs that are changing will require model accuracies of better than 0.1 W/m^2. Projections with any degree of precision for multiple decades, may require sensitivities to the various forcings another order of magnitude more accurate.

We haven’t had observations accurate enough to validate models to such accuracy for more than a couple decades, if that.

The IPCC diagnostic subprojects have shown the models to have errors of multiple watts per meter squared (e.g. Roesch 2006), and to the extent that they manage to balance energy budgets to observations, there must be compensating errors of a similar magnitude in effect in ajustible parameters.

Yes, climate is predictable, but the IPCC diagnostic subprojects show that predictive capability is not here yet, although, the optimisitic among us, hope it is just a few years away.